{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random as ran\n",
    "# Import MNIST data\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Training Images in Dataset = (55000, 784)\n",
      "--------------------------------------------------\n",
      "x_train Examples Loaded = (55000, 784)\n",
      "y_train Examples Loaded = (55000, 10)\n",
      "\n",
      "[ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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By4FVkpYCm4EFrSnRzFphyPBHxJ2ABmk+pbnlmFm7+BN+Zply+M0y5fCbZcrhN8uUw2+W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\n5fCbZcrhN8vU/wNuf5iCOfWglAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1132e6fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KS74+BuGUeirQt29fjS8jgga44xlOncleFHgN/QuCnJwc1K1b17ZrkcCPP/6I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\nhQsXLmo5/j97tD/dMA8wSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1132e6f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def train_size(num):\n",
    "    print ('Total Training Images in Dataset = ' + str(mnist.train.images.shape))\n",
    "    print ('--------------------------------------------------')\n",
    "    x_train = mnist.train.images[:num,:]\n",
    "    print ('x_train Examples Loaded = ' + str(x_train.shape))\n",
    "    y_train = mnist.train.labels[:num,:]\n",
    "    print ('y_train Examples Loaded = ' + str(y_train.shape))\n",
    "    print('')\n",
    "    return x_train, y_train\n",
    "\n",
    "def test_size(num):\n",
    "    print ('Total Test Examples in Dataset = ' + str(mnist.test.images.shape))\n",
    "    print ('--------------------------------------------------')\n",
    "    x_test = mnist.test.images[:num,:]\n",
    "    print ('x_test Examples Loaded = ' + str(x_test.shape))\n",
    "    y_test = mnist.test.labels[:num,:]\n",
    "    print ('y_test Examples Loaded = ' + str(y_test.shape))\n",
    "    return x_test, y_test\n",
    "\n",
    "def display_digit(num):\n",
    "    print(y_train[num])\n",
    "    label = y_train[num].argmax(axis=0)\n",
    "    image = x_train[num].reshape([28,28])\n",
    "    plt.title('Example: %d  Label: %d' % (num, label))\n",
    "    plt.imshow(image, cmap=plt.get_cmap('gray_r'))\n",
    "    plt.show()\n",
    "\n",
    "def display_mult_flat(start, stop):\n",
    "    images = x_train[start].reshape([1,784])\n",
    "    for i in range(start+1,stop):\n",
    "        images = np.concatenate((images, x_train[i].reshape([1,784])))\n",
    "    plt.imshow(images, cmap=plt.get_cmap('gray_r'))\n",
    "    plt.show()\n",
    "\n",
    "x_train, y_train = train_size(55000)\n",
    "display_digit(ran.randint(0, x_train.shape[0]))\n",
    "display_mult_flat(0,400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "learning_rate = 0.001\n",
    "training_iters = 500\n",
    "batch_size = 128\n",
    "display_step = 10\n",
    "# Network Parameters\n",
    "n_input = 784 # MNIST data input (img shape: 28*28)\n",
    "n_classes = 10 # MNIST total classes (0-9 digits)\n",
    "dropout = 0.85 # Dropout, probability to keep units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, [None, n_input])\n",
    "y = tf.placeholder(tf.float32, [None, n_classes])\n",
    "keep_prob = tf.placeholder(tf.float32) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def conv2d(x, W, b, strides=1):\n",
    "    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')\n",
    "    x = tf.nn.bias_add(x, b)\n",
    "    return tf.nn.relu(x)\n",
    "\n",
    "\n",
    "def maxpool2d(x, k=2):\n",
    "    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],\n",
    "                          padding='SAME')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def conv_net(x, weights, biases, dropout):\n",
    "    # reshape the input picture\n",
    "    x = tf.reshape(x, shape=[-1, 28, 28, 1])\n",
    "\n",
    "    # First convolution layer\n",
    "    conv1 = conv2d(x, weights['wc1'], biases['bc1'])\n",
    "    # Max Pooling used for downsampling\n",
    "    conv1 = maxpool2d(conv1, k=2)\n",
    "\n",
    "    # Second convolution layer\n",
    "    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])\n",
    "    # Max Pooling used for downsampling\n",
    "    conv2 = maxpool2d(conv2, k=2)\n",
    "\n",
    "    # Reshape conv2 output to matcht the input of fully connected layer \n",
    "    fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])\n",
    "\n",
    "    # Fully connected layer\n",
    "    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])\n",
    "    fc1 = tf.nn.relu(fc1)\n",
    "    \n",
    "    # Dropout\n",
    "    fc1 = tf.nn.dropout(fc1, dropout)\n",
    "\n",
    "    # Output the class prediction\n",
    "    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weights = {\n",
    "    # 5x5 conv, 1 input, and 32 outputs\n",
    "    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),\n",
    "    # 5x5 conv, 32 inputs, and 64 outputs\n",
    "    'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),\n",
    "    # fully connected, 7*7*64 inputs, and 1024 outputs\n",
    "    'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),\n",
    "    # 1024 inputs, 10 outputs for class digits\n",
    "    'out': tf.Variable(tf.random_normal([1024, n_classes]))\n",
    "}\n",
    "\n",
    "biases = {\n",
    "    'bc1': tf.Variable(tf.random_normal([32])),\n",
    "    'bc2': tf.Variable(tf.random_normal([64])),\n",
    "    'bd1': tf.Variable(tf.random_normal([1024])),\n",
    "    'out': tf.Variable(tf.random_normal([n_classes]))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = conv_net(x, weights, biases, keep_prob)\n",
    "\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n",
    "\n",
    "correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter 10, Minibatch Loss= 27436.70, Training Accuracy= 0.12\n",
      "Testing Accuracy:0.12\n",
      "Iter 20, Minibatch Loss= 8442.39, Training Accuracy= 0.51\n",
      "Testing Accuracy:0.51\n",
      "Iter 30, Minibatch Loss= 7687.42, Training Accuracy= 0.58\n",
      "Testing Accuracy:0.58\n",
      "Iter 40, Minibatch Loss= 5625.63, Training Accuracy= 0.67\n",
      "Testing Accuracy:0.67\n",
      "Iter 50, Minibatch Loss= 4521.98, Training Accuracy= 0.71\n",
      "Testing Accuracy:0.71\n",
      "Iter 60, Minibatch Loss= 2594.59, Training Accuracy= 0.81\n",
      "Testing Accuracy:0.81\n",
      "Iter 70, Minibatch Loss= 2250.65, Training Accuracy= 0.85\n",
      "Testing Accuracy:0.85\n",
      "Iter 80, Minibatch Loss= 2743.55, Training Accuracy= 0.84\n",
      "Testing Accuracy:0.84\n",
      "Iter 90, Minibatch Loss= 1642.03, Training Accuracy= 0.89\n",
      "Testing Accuracy:0.89\n",
      "Iter 100, Minibatch Loss= 2167.67, Training Accuracy= 0.84\n",
      "Testing Accuracy:0.84\n",
      "Iter 110, Minibatch Loss= 1964.33, Training Accuracy= 0.87\n",
      "Testing Accuracy:0.87\n",
      "Iter 120, Minibatch Loss= 1928.49, Training Accuracy= 0.85\n",
      "Testing Accuracy:0.85\n",
      "Iter 130, Minibatch Loss= 515.65, Training Accuracy= 0.95\n",
      "Testing Accuracy:0.95\n",
      "Iter 140, Minibatch Loss= 1025.47, Training Accuracy= 0.93\n",
      "Testing Accuracy:0.93\n",
      "Iter 150, Minibatch Loss= 1473.18, Training Accuracy= 0.91\n",
      "Testing Accuracy:0.91\n",
      "Iter 160, Minibatch Loss= 959.78, Training Accuracy= 0.91\n",
      "Testing Accuracy:0.91\n",
      "Iter 170, Minibatch Loss= 1723.55, Training Accuracy= 0.88\n",
      "Testing Accuracy:0.88\n",
      "Iter 180, Minibatch Loss= 1495.15, Training Accuracy= 0.89\n",
      "Testing Accuracy:0.89\n",
      "Iter 190, Minibatch Loss= 444.74, Training Accuracy= 0.95\n",
      "Testing Accuracy:0.95\n",
      "Iter 200, Minibatch Loss= 778.06, Training Accuracy= 0.95\n",
      "Testing Accuracy:0.95\n",
      "Iter 210, Minibatch Loss= 758.30, Training Accuracy= 0.92\n",
      "Testing Accuracy:0.92\n",
      "Iter 220, Minibatch Loss= 614.96, Training Accuracy= 0.92\n",
      "Testing Accuracy:0.92\n",
      "Iter 230, Minibatch Loss= 1447.76, Training Accuracy= 0.90\n",
      "Testing Accuracy:0.90\n",
      "Iter 240, Minibatch Loss= 1072.54, Training Accuracy= 0.93\n",
      "Testing Accuracy:0.93\n",
      "Iter 250, Minibatch Loss= 1048.84, Training Accuracy= 0.91\n",
      "Testing Accuracy:0.91\n",
      "Iter 260, Minibatch Loss= 909.37, Training Accuracy= 0.92\n",
      "Testing Accuracy:0.92\n",
      "Iter 270, Minibatch Loss= 1421.38, Training Accuracy= 0.88\n",
      "Testing Accuracy:0.88\n",
      "Iter 280, Minibatch Loss= 1513.82, Training Accuracy= 0.90\n",
      "Testing Accuracy:0.90\n",
      "Iter 290, Minibatch Loss= 709.25, Training Accuracy= 0.94\n",
      "Testing Accuracy:0.94\n",
      "Iter 300, Minibatch Loss= 734.91, Training Accuracy= 0.96\n",
      "Testing Accuracy:0.96\n",
      "Iter 310, Minibatch Loss= 455.21, Training Accuracy= 0.96\n",
      "Testing Accuracy:0.96\n",
      "Iter 320, Minibatch Loss= 892.19, Training Accuracy= 0.96\n",
      "Testing Accuracy:0.96\n",
      "Iter 330, Minibatch Loss= 1344.86, Training Accuracy= 0.91\n",
      "Testing Accuracy:0.91\n",
      "Iter 340, Minibatch Loss= 1383.25, Training Accuracy= 0.90\n",
      "Testing Accuracy:0.90\n",
      "Iter 350, Minibatch Loss= 566.65, Training Accuracy= 0.95\n",
      "Testing Accuracy:0.95\n",
      "Iter 360, Minibatch Loss= 455.21, Training Accuracy= 0.95\n",
      "Testing Accuracy:0.95\n",
      "Iter 370, Minibatch Loss= 1124.65, Training Accuracy= 0.93\n",
      "Testing Accuracy:0.93\n",
      "Iter 380, Minibatch Loss= 477.87, Training Accuracy= 0.95\n",
      "Testing Accuracy:0.95\n",
      "Iter 390, Minibatch Loss= 218.40, Training Accuracy= 0.98\n",
      "Testing Accuracy:0.98\n",
      "Iter 400, Minibatch Loss= 411.34, Training Accuracy= 0.95\n",
      "Testing Accuracy:0.95\n",
      "Iter 410, Minibatch Loss= 862.68, Training Accuracy= 0.92\n",
      "Testing Accuracy:0.92\n",
      "Iter 420, Minibatch Loss= 1324.55, Training Accuracy= 0.91\n",
      "Testing Accuracy:0.91\n",
      "Iter 430, Minibatch Loss= 392.94, Training Accuracy= 0.96\n",
      "Testing Accuracy:0.96\n",
      "Iter 440, Minibatch Loss= 993.16, Training Accuracy= 0.93\n",
      "Testing Accuracy:0.93\n",
      "Iter 450, Minibatch Loss= 788.18, Training Accuracy= 0.94\n",
      "Testing Accuracy:0.94\n",
      "Iter 460, Minibatch Loss= 257.56, Training Accuracy= 0.97\n",
      "Testing Accuracy:0.97\n",
      "Iter 470, Minibatch Loss= 1108.51, Training Accuracy= 0.92\n",
      "Testing Accuracy:0.92\n",
      "Iter 480, Minibatch Loss= 211.95, Training Accuracy= 0.97\n",
      "Testing Accuracy:0.97\n",
      "Iter 490, Minibatch Loss= 668.19, Training Accuracy= 0.95\n",
      "Testing Accuracy:0.95\n",
      "Iter 500, Minibatch Loss= 164.29, Training Accuracy= 0.97\n",
      "Testing Accuracy:0.97\n"
     ]
    }
   ],
   "source": [
    "train_loss = []\n",
    "train_acc = []\n",
    "test_acc = []\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    step = 1\n",
    "    while step <= training_iters:\n",
    "        batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
    "        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,\n",
    "                                       keep_prob: dropout})\n",
    "        if step % display_step == 0:\n",
    "            loss_train, acc_train = sess.run([cost, accuracy], \n",
    "                                             feed_dict={x: batch_x,\n",
    "                                                        y: batch_y,\n",
    "                                                        keep_prob: 1.})\n",
    "            print \"Iter \" + str(step) + \", Minibatch Loss= \" + \\\n",
    "                  \"{:.2f}\".format(loss_train) + \", Training Accuracy= \" + \\\n",
    "                  \"{:.2f}\".format(acc_train)\n",
    "    \n",
    "            # Calculate accuracy for mnist test images. \n",
    "            # Note that in this case no dropout\n",
    "            acc_test = sess.run(accuracy, \n",
    "                                feed_dict={x: mnist.test.images,\n",
    "                                      y: mnist.test.labels,\n",
    "                                      keep_prob: 1.})\n",
    "    \n",
    "            print \"Testing Accuracy:\" + \\\n",
    "               \"{:.2f}\".format(acc_train)\n",
    "    \n",
    "            train_loss.append(loss_train)\n",
    "            train_acc.append(acc_train)\n",
    "            test_acc.append(acc_test)\n",
    "            \n",
    "        step += 1\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BgwdXU5QiIhEllRquW7du9OzZk9tuu4327dtzyy23sGTJklLrbN++nfvvv5+O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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11434c050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+mmlCAKdtYciQITzwwAO0aNGCjRs3MnHiREsIxpic8eR24lr65Ifqo3889JAq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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114332c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval_indices = range(0, training_iters, display_step)\n",
    "# Plot loss over time\n",
    "plt.plot(eval_indices, train_loss, 'k-')\n",
    "plt.title('Softmax Loss per iteration')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Softmax Loss')\n",
    "plt.show()\n",
    "# Plot train and test accuracy\n",
    "plt.plot(eval_indices, train_acc, 'k-', label='Train Set Accuracy')\n",
    "plt.plot(eval_indices, test_acc, 'r--', label='Test Set Accuracy')\n",
    "plt.title('Train and Test Accuracy')\n",
    "plt.xlabel('Generation')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
